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Genetic algorithm based geometry optimization of inductively coupled printed spiral coils for remote powering of electronic implantable devices

机译:基于遗传算法基于电感耦合印刷螺旋线圈的几何优化,用于远程供电的电子植入装置

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Electronic biomedical implantable devices need powering to perform. Among the main reported approaches, inductive links are the most commonly used method for remote powering of such devices. Power efficiency is the most important characteristic to be considered when designing inductive links to transfer energy to implantable devices. The maximum power efficiency is obtained for maximum coupling and quality factors of the coils and is generally limited as the coupling between the inductors is usually very small. This paper is dealing with geometry optimization of inductively coupled printed spiral coils for the powering of a given implant system. For this aim, simple mathematical models that approximate coil parameters and link efficiency are derived, and using these models two different approaches are used to provide optimal coil geometries for a maximum efficiency of the link. First an iterative design procedure is implemented then genetic based algorithm optimisation is derived to find the optimal coil geometries of the used coil structure. Theoretical results are verified by simulation using HFSS software. A comparative analysis confirmed the effectiveness of the genetic algorithm based approach to provide the optimal coil geometries.
机译:电子生物医学可植入设备需要进行动力。在主要报告的方法中,归纳链接是用于这些设备的远程供电的最常用方法。功率效率是设计诱导链接以将能量转移到可植入设备时最重要的特征。获得最大功率效率以获得线圈的最大耦合和质量因子,并且通常被限制为电感器之间的耦合通常非常小。本文正在处理电感耦合印刷螺旋线圈的几何优化,用于给定植入系统的供电。为此目的,推导出近似线圈参数和链路效率的简单数学模型,并使用这些模型进行两种不同的方法,用于提供最大效率的最佳线圈几何形状。首先,实现迭代设计过程,然后导出基于遗传基于遗传算法优化,以找到所用线圈结构的最佳线圈几何形状。通过使用HFSS软件的仿真验证了理论结果。比较分析证实了基于遗传算法的效果提供了最佳线圈几何形状。

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